Retrospective analysis of breast radiotherapy treatment plans: Curating the ‘non‐curated’

Abstract

Introduction: This paper provides a demonstration of how non-curated data can be retrospectively cleaned, so that existing repositories of radiotherapy treatment planning data can be used to complete bulk retrospective analyses of dosimetric trends and other plan characteristics. Methods: A non curated archive of 1137 radiotherapy treatment plans accumulated over a 12-month period, from five radiotherapy centres operated by one institution, was used to investigate and demonstrate a process of clinical data cleansing, by: identifying and translating inconsistent structure names; correcting inconsistent lung contouring; excluding plans for treatments other than breast tangents and plans without identifiable PTV, lung and heart structures; and identifying but not excluding plans that deviated from the local planning protocol. PTV, heart and lung dose-volume metrics were evaluated, in addition to a sample of personnel and linac load indicators. Results: Data cleansing reduced the number of treatment plans in the sample by 35.7%. Inconsistent structure names were successfully identified and translated (e.g. 35 different names for lung). Automatically separating whole lung structures into left and right lung structures allowed the effect of contralateral and ipsilateral lung dose to be evaluated, while introducing some small uncertainties, compared to manual contouring. PTV doses were indicative of prescription doses. Breast treatment work was unevenly distributed between oncologists and between metropolitan and regional centres. Conclusion: This paper exemplifies the data cleansing and data analysis steps that may be completed using existing treatment planning data, to provide individual radiation oncology departments with access to information on their own patient populations. Clearly, the well-planned and systematic recording of new, high quality data is the preferred solution, but the retrospective curation of non-curated data may be a useful interim solution, for radiation oncology departments where the systems for recording of new data have yet to be designed and agreed.

Scalable radiotherapy data curation infrastructure for deep-learning based autosegmentation of organs-at-risk: A case study in head and neck cancer

“…It is difficult to find reports like ours in the literature. Our review uncovered one prior study pertaining to curation infrastructure for image labeling in the context of radiology ( 32 ) and another prior study related to retrospective curation of breast DICOM-RT data for downstream correlation with dosimetric endpoints ( 33 ). Somewhat related, Wong et al.…”

“…It is difficult to find reports like ours in the literature. Our review uncovered one prior study pertaining to curation infrastructure for image labeling in the context of radiology (32) and another prior study related to retrospective curation of breast DICOM-RT data for downstream correlation with dosimetric endpoints (33). Somewhat related, Wong et al described their RT patient selection and expert OAR segmentation efforts towards DL-based autosegmentation model evaluation, inclusive of capturing inter-expert variability (20).…”

Impact of radiopacified bone cement on radiotherapy dose calculation

“…Minimum, median and maximum dose values and homogeneity index (HI, defined as HI = [D 2% -D 98% ]/D 50% ) in the PTV were extracted from the calculated dose distributions using the Treatment and Dose Assessor (TADA) software [13,14]. A boolean difference (subtraction) operation was performed to obtain a planning target volume minus any overlapping flap volume ("PTV minus flap"), for calculation of dose-volume metrics exclusive of any dose difference in the bone flap.…”